code: def process(
      self, sparse_data: List[Dict[int, float]]
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      missing_value = normalization.MISSING_VALUE
      if self.set_missing_value_to_zero:
          missing_value = 0.0
      values = torch.nan_to_num(
          torch.FloatTensor(
              [
                  [
                      row[col] if col in row else missing_value
                      for col in self.sorted_features
                  ]
                  for row in sparse_data
              ]
          ),
          nan=missing_value,
      )
      if self.set_missing_value_to_zero:
          # When we set missing values to 0, we don't know what is and isn't missing
          presence = torch.ones_like(values, dtype=torch.bool)
      else:
          presence = values != missing_value
      return values, presence
  
file_path: ReAgent/reagent/preprocessing/sparse_to_dense.py
imports: from collections import defaultdict
  from typing import Dict, List, Tuple
  import torch
  from reagent.preprocessing import normalization
label_desc: Use image node_id 2 for calling the PythonSparseToDenseProcessor obj's underlying member method code with example usage: obj.process(sparse_data) and returns: values,  presence